{"paper":{"title":"Quality Assessment of Spectroscopic Data Reduction Pipelines Using Artificial Intelligence: Scrutinizing Data Release 2 from the DESI Survey","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.IM","authors_text":"A. de la Macorra, A. Font-Ribera, A. Kremin, A. Meisner, B. A. Weaver, B. Dey, C. Howlett, D. Bianchi, D. Brooks, D. Kirkby, D. Schlegel, E. Gaztanaga, E. Sanchez, F. Prada, G. Gutierrez, G. Rossi, G. Tarle, H. Seo, H. Zou, I. Perez-Rafols, J. Aguilar, J. E. Forero-Romero, J. Moustakas, J. Silber, J. Suarez-Perez, K. Honscheid, L. Le Guillou, L. Samushia, M. E. Levi, M. Landriau, M. Manera, N. Palanque-Delabrouille, O. Lahav, P. Doel, R. Joyce, R. Miquel, R. P. Nathan, R. Sharples, S. Ahlen, S. Bailey, S. Ferraro, S. Gontcho A Gontcho, S. Juneau, S. Nadathur, S. Panda, T. Claybaugh, V. Torres-Gomez, W. J. Percival","submitted_at":"2026-06-19T01:58:25Z","abstract_excerpt":"Large spectroscopic surveys now collect data at a scale that makes traditional visual inspection impractical. We present an unsupervised pipeline for spectroscopic quality assessment that requires no labeled training data. The method combines Uniform Manifold Approximation and Projection for dimensionality reduction with Friends-of-Friends clustering to isolate anomalous spectra for targeted review. We apply this pipeline to 58,291,334 spectra across 14,199 tiles from DESI Data Release 2, processing each tile independently to produce a tile-level outlier catalog. In each tile, the pipeline sep"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.21035","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.21035/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}